Fish and Fisheries最新文献

Age-structured models are used worldwide to regulate fisheries. These models typically ignore top-down interactions (predation affecting natural mortality) and bottom-up interactions (consumption affecting individual growth, reproduction, or survival), whereas multispecies catch-at-age models often incorporate top-down but not bottom-up interactions. While Ecopath-with-Ecosim (EwE) incorporates both top-down and bottom-up interactions along with age-structured dynamics, it is not typically fitted to age-composition data. We extend Ecostate (a state-space version of EwE) to incorporate age-structured dynamics while fitting to age-structured data and use this to illustrate how to add bottom-up interactions to age-structured models. Specifically, we add age-structured dynamics and likelihood components for age-composition and weight-at-age data while estimating residual variation in larval survival (recruitment deviations) and consumption (weight-at-age deviations). As a demonstration, we fit the model to biomass and age-composition data for two commercial species (Alaska pollock and sablefish) in the Gulf of Alaska, including population dynamics for their major prey, while not fitting weight-at-age data so that it can be used for out-of-sample evaluation of model performance. The model can be viewed as a multispecies age-structured model (e.g., estimating adult mortality rates, survey catchability and selectivity, and recruitment variation) and as a mass-balance ecosystem model (e.g., estimating trophic position and weight-at-age based on forage consumption). The predicted weight-at-age is weakly correlated with independent measurements for pollock and sablefish but was improved when we incorporated forage biomass indices. We recommend that age-structured models routinely explore the link between prey consumption and resulting size-at-age, whether using coupled predator–prey dynamics or simplifications that treat prey abundance as fixed data.

Recreational fishing is widespread in coastal zones and exerts significant ecological, fisheries-related and socio-economic pressures. Unlike commercial fishing, small-scale recreational fleets are challenging to monitor because they lack enforced use of vessel tracking systems such as the Automatic Identification System (AIS). Recently, remote sensing technologies have emerged as promising alternatives for monitoring marine activities. Here, we assess the potential of high spatio-temporal resolution satellite imagery to monitor daily changes in recreational fishing boats during a temporal fishing ban within a marine protected area. By comparing satellite-derived boat detections with AIS records, we demonstrate that satellite data can reliably capture daily changes in recreational fishing activity missed by AIS, including a marked increase immediately following the end of the ban. These findings confirm that satellite observations can consistently detect small fishing boats and reveal their fine-scale spatio-temporal patterns. When complemented with local knowledge, this approach enhances our capacity to contribute to the spatial planning and ecosystem-based management of recreational fisheries.

Climate change is expected to have significant impacts on the biology, abundance and distribution of transboundary fish stocks, not only among neighbouring countries within the jurisdictions of regional fisheries management organisations (RFMOs) but also between adjacent RFMOs. Using South Pacific albacore tuna (Thunnus alalunga) as a case study, we highlight how RFMOs need to understand the impacts of climate change on transboundary stocks under their purview with greater certainty. We identify four areas of research that should assist RFMOs to adapt their scientific processes—strengthened understanding of changes in the biology of target stocks; enhanced collection of data to support modelling; improved modelling of catch-per-unit of effort (CPUE) to better reflect climate change impacts on stock abundance for assessments; and ensuring that scientific advice is adaptive and robust to climate change, including through implementation of tested harvest strategies. Investments in these research areas should enable RFMOs to improve the science underpinning management measures designed to sustain transboundary stocks and increase fishery performance during climate change.

Fisheries stock assessment capacity faces resource constraints in many countries, including limited personnel, high workloads, and restricted funding. Stock assessment scientists often operate under short timelines, with outcomes that can influence livelihoods and receive public scrutiny. Scientists frequently manage multiple assessments each year, making time management a challenge. Consequently, finding opportunities to invest in professional development remains difficult within existing operational demands. We developed a series of approaches to invest in Australia's employed stock assessment capacity, and present these as a case study for other similar marine science fields and countries. The approaches led to critical insights to a way forward to develop a ‘Community of Practice’. We used network analyses to evaluate collaboration based on published Australian stock assessments and related papers. We then used surveys and interviews to understand the factors that have constrained stock assessment scientists in investing in their own development. Tools and opportunities were then made available: (a) a web site providing a central repository on freely available stock assessment packages and Australian stock assessment reports; (b) training courses on the use of assessment packages that were open to participants across organisations; and (c) a simulation game to learn in a ‘consequence free’ environment. This paper highlights what has been learnt and generalises these findings beyond the Australian stock assessment community's well-being.

Citizen science is increasingly harnessed worldwide to gather data otherwise requiring a prohibitive investment of funding and time. Meanwhile, the revolution in digital communication offers opportunities from crowdsourcing, big data approaches and social network mining to quickly and cost-effectively fill major gaps in knowledge necessary to protect endangered populations. Sharks are among the most endangered and data-poor vertebrates in the ocean. Mainly due to overfishing, many shark populations are declining worldwide, while most species lack basic abundance, distribution and life-history data. Hence, filling knowledge gaps across taxa, ecosystems, and regions is urgently needed to increase our understanding of their ecology, develop effective conservation actions and reverse their loss. Here, we introduce a novel citizen science and crowdsourcing approach for conservation through sharkPulse, a new platform automating data ingestion and organisation to build the largest database of shark occurrence records to date. Designed to complement and extend similar biodiversity monitoring tools relying heavily on user submissions, sharkPulse aims to source large streams of online shark images and transform them into occurrence records, filling knowledge gaps in shark ecology and biology. This platform offers a blueprint to leverage AI and big data approaches, social network data mining and participatory science to efficiently and continuously source visual media materials and transform the monitoring of data-limited marine and terrestrial animal populations.